Air pollution caused by small particles is a major public health problem in many cities of the world. One of the most contaminated cities is Mexico City. The fact that it is located in a volcanic crater surrounded by mountains helps thermal inversion and imply a huge pollution problem by trapping a thick
layer of smog that float over the city. Modeling air pollution is a political and
administrative important issue due to the fact that the prediction of critical events should guide decision making. The need for countermeasures against such episodes requires predicting with accuracy and in advance relevant indicators of air pollution, such are particles smaller than 2.5 microns (PM 2.5). In this work two different fuzzy approaches for modeling PM
2.5 concentrations in Mexico City metropolitan area are compared with respect the simple persistence method.